Trends in Neurosciences
Review
INMED/TINS special issueAnalysis of dynamic brain oscillations: methodological advances
INMED/TINS special issue
Section snippets
Introduction: the complex web of neuronal oscillations
Advances in our understanding of neural systems go hand-in-hand with improvements in experimental techniques used to study these systems. From the rapid growth in biotechnology, multisite recording techniques now enable monitoring of ensemble oscillations in great detail by simultaneously recording local neuronal activity from a large number of network locations [1] (Box 1). What emerges from these parallel neuronal recordings is a rich picture of brain dynamics, in which locally generated
Time–frequency structures of dynamic oscillations
The key to extracting information from a set of measurements is to display those measurements in another equivalent representation in which their information content becomes obvious. Often, the key to extracting information is to switch from a temporal domain to a frequency domain. The first such transformation in wide use is the Fourier transform, providing spectral power that identifies the amplitudes of sine functions of various frequencies that exist throughout the entire duration of the
Phase synchronization between neuronal oscillations
Simultaneous recording of multiple oscillations within and between different cortical regions offers insight into how distributed neuronal oscillations work together to generate complex brain functions. Recently, there has been a series of remarkable results showing that interaction dynamics between spatially distributed neuronal oscillations can be exploited for studying large-scale ‘functional integration’, that is, the transient integration of numerous neuronal ensembles that are widely
Ensemble synchronization: seeing both the forest and the trees
Most synchronization methods are defined for pairs of recording channels only. A global picture of synchronization in multichannel data can be obtained by averaging the pairwise synchronization between every possible pair of channels. The main problem with these pairwise correlations is their inability to detect ensemble synchronization over larger cortical areas because they are present in traveling oscillations. This is owing to the restriction of pairwise analysis and temporal averaging,
Causal networks of neuronal oscillations
Analysis of synchronization alone does not address the question of causality between two oscillations, the so-called ‘effective connectivity’ defining the influence one neuronal system exerts on another (‘who drives whom’) [46]. Traditional measures, such as cross-correlation, can, in principle, indicate the delay in coupling, but inferring causality from the time delay is not completely satisfactory [47]. Furthermore, two oscillations in a network do not have to interact directly. Therefore,
Cross-frequency coupling between oscillations
Investigating the interaction between different frequencies adds another dimension to the already complex identification of spatiotemporal and frequency-specific neuronal networks. In this context, direct cortical recordings reveal that cross-frequency couplings between distinct brain regions are abundant, most prominently as an interaction between low and high frequencies (e.g. Refs 58, 59, 60, 61, 62, 63), often mediating top-down modulating, ‘attentional’ or other context-defining functions 5
Concluding remarks
Here, we have outlined analysis protocols for oscillations in multichannel data. These tools have tremendous potential for studying oscillations in multisite recordings, enabling revelation of complex, dynamic relationships that cannot be derived from simple plots and statistics. In particular, these computational and analytical tools can be a powerful aid to making sense of the data, which, in turn, can influence the experimentation.
Nevertheless, it is not possible to develop ‘black box’-like
Acknowledgements
We thank G. Buzsaki, O. Paulsen, O. David, J.P. Lachaux and R. Staba for reading the article.
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